120 lines
4.5 KiB
Python
120 lines
4.5 KiB
Python
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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"""A streamable transformer."""
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import typing as tp
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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def create_sin_embedding(positions: torch.Tensor, dim: int, max_period: float = 10000):
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"""Create time embedding for the given positions, target dimension `dim`.
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"""
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# We aim for BTC format
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assert dim % 2 == 0
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half_dim = dim // 2
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adim = torch.arange(half_dim, device=positions.device).view(1, 1, -1)
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phase = positions / (max_period ** (adim / (half_dim - 1)))
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return torch.cat([
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torch.cos(phase),
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torch.sin(phase),
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], dim=-1)
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class StreamingTransformerEncoderLayer(nn.TransformerEncoderLayer):
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def forward(self, x: torch.Tensor, x_past: torch.Tensor, past_context: int): # type: ignore
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if self.norm_first:
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sa_input = self.norm1(x)
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x = x + self._sa_block(sa_input, x_past, past_context)
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x = x + self._ff_block(self.norm2(x))
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else:
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sa_input = x
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x = self.norm1(x + self._sa_block(sa_input, x_past, past_context))
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x = self.norm2(x + self._ff_block(x))
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return x, sa_input
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# self-attention block
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def _sa_block(self, x: torch.Tensor, x_past: torch.Tensor, past_context: int): # type: ignore
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_, T, _ = x.shape
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_, H, _ = x_past.shape
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queries = x
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keys = torch.cat([x_past, x], dim=1)
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values = keys
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queries_pos = torch.arange(H, T + H, device=x.device).view(-1, 1)
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keys_pos = torch.arange(T + H, device=x.device).view(1, -1)
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delta = queries_pos - keys_pos
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valid_access = (delta >= 0) & (delta <= past_context)
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x = self.self_attn(queries, keys, values,
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attn_mask=~valid_access,
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need_weights=False)[0]
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return self.dropout1(x)
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class StreamingTransformerEncoder(nn.Module):
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"""TransformerEncoder with streaming support.
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Args:
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dim (int): dimension of the data.
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hidden_scale (int): intermediate dimension of FF module is this times the dimension.
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num_heads (int): number of heads.
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num_layers (int): number of layers.
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max_period (float): maxium period of cosines in the positional embedding.
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past_context (int or None): receptive field for the causal mask, infinite if None.
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gelu (bool): if true uses GeLUs, otherwise use ReLUs.
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norm_in (bool): normalize the input.
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dropout (float): dropout probability.
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**kwargs: See `nn.TransformerEncoderLayer`.
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"""
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def __init__(self, dim, hidden_scale: float = 4., num_heads: int = 8, num_layers: int = 5,
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max_period: float = 10000, past_context: int = 1000, gelu: bool = True,
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norm_in: bool = True, dropout: float = 0., **kwargs):
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super().__init__()
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assert dim % num_heads == 0
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hidden_dim = int(dim * hidden_scale)
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self.max_period = max_period
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self.past_context = past_context
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activation: tp.Any = F.gelu if gelu else F.relu
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self.norm_in: nn.Module
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if norm_in:
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self.norm_in = nn.LayerNorm(dim)
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else:
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self.norm_in = nn.Identity()
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self.layers = nn.ModuleList()
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for idx in range(num_layers):
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self.layers.append(
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StreamingTransformerEncoderLayer(
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dim, num_heads, hidden_dim,
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activation=activation, batch_first=True, dropout=dropout, **kwargs))
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def forward(self, x: torch.Tensor,
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states: tp.Optional[tp.List[torch.Tensor]] = None,
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offset: tp.Union[int, torch.Tensor] = 0):
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B, T, C = x.shape
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if states is None:
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states = [torch.zeros_like(x[:, :1]) for _ in range(1 + len(self.layers))]
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positions = torch.arange(T, device=x.device).view(1, -1, 1) + offset
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pos_emb = create_sin_embedding(positions, C, max_period=self.max_period)
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new_state: tp.List[torch.Tensor] = []
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x = self.norm_in(x)
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x = x + pos_emb
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for layer_state, layer in zip(states, self.layers):
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x, new_layer_state = layer(x, layer_state, self.past_context)
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new_layer_state = torch.cat([layer_state, new_layer_state], dim=1)
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new_state.append(new_layer_state[:, -self.past_context:, :])
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return x, new_state, offset + T
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